Trialling a convolution neural network for the identification of Braconidae in New Zealand

Abstract

Computer vision approaches, such as deep learning, potentially offer a range of benefits to entomology, particularly for the image-based identification of taxa. An experiment was conducted to gauge the ability of a convolution neural network (CNN) to identify genera of Braconidae from images of forewings. A deep learning CNN was trained via transfer learning from a small set of 488 images for 57 genera. Three-fold cross-validation achieved an accuracy of 96.7%, thus demonstrating that identification to genus using forewings is highly predictive. Further work is needed to increase both the coverage to species level and the number of images available.